Archive

Finally worked this out : how to use sparql queries against nepomuk to give a file list.

In the “places” tab of Dolphin you will see “Search For” section on the left hand side, with items like Documents, Images, Audio Files. Each of these contains a simple search query for Nepomuk. What do you do if you want something more complicated – a file type or file name regexp ? The former would cover not just file extensions, but anything that nepomuk regards as a file that “is a” something – eg “is a” archive type. The later needs to look at the filename label and filter.

After much messing about, trying to find the nepomuk log[1], looking at the ontology in Protege etc heres what I came up with. For each expression you want to use, right click on “Search For” and choose “add entry”. Now give your expression a name, then in the Location field add the sparql query, using the nepomuk query protocol prefix. For example –

This last one took a while to work out because of the wildcard and the literal dot and basic regexp syntax is easy to forget if you don’t use it everyday 😉 Haven’t tried this with the bif syntax, but it should work if virtuoso is doing its thing.

The nepomuk ontology is not too complicated to once you remember the basics of sparql and regex, but it would also be good to have a Nepomuk sparql console in KDE to help set these things up and get feedback. I found one Nepomuk log (~/.kde/share/apps/nepomuk/repository/main/data/virtuosobackend/soprano-virtuoso.log), but I’m not sure its what would be needed in this case – it seemed to contain some syntax error messages that would have helped me, but also didnt seem to update in real time.

Another good reference is the KDE Nepomuk Manual blog : http://kdenepomukmanual.wordpress.com/. This explains Nepomuk integration in KDE and how it can be used, including the tag:/ protocol. Check out the Nepomuk tag manager at http://techbase.kde.org/Projects/Nepomuk/Repositories#Tag_Manager as well. (Further browsing here reveals a sparql console as well – the Nepomuk Shell).

Speaking of protocols, you can also use timeline:/ to get an historical inventory view of changes in Dolphin. (http://www.chakra-project.org/wiki/index.php?title=Nepomuk). For a list of protocols, expand the downarrow control to the very left of Dolphins search bar (when you clear the search bar that is) – timeline:/ and nepomuksearch:/ are listed under “other”.

I am doing some work on a Top Secret Project to demonstrate on the SkyTwenty[1] platform the use of email data (in place of location data).

I am making use of Aperture[2] to crawl an IMAP store, then allow sharing of contact and message information, so that queries can be run to discover

who-knows-who in what domain

how many degrees of freedom there are between contacts

do selected contacts have any connection

how “well” do they know each other and so on.

Aperture makes use of the Nepomuk [3] message and desktop ontologies[4], and they’re fairly extensive, so a graphic helps to understand some of the ontological relationships.

The brilliant Protege4 [5] ontology design tool has plugins for GraphViz[6] and OntoGraf[7] produce some fairly neat images to visualise ontologies, so here they are. I would like if there was a way to include object and data propertys (by annotation perhaps, will try later) but for now have compiled a table of the class properties from a crawl and sparql query I did against the repository I loaded the data into.

Contact class relationships

Note that OntoGraf needs the Sun JDK to work, so on Ubuntu, which has the OpenJDK by default, you need to install and agree to the license terms, then make sure that Protege is using the Sun java at /usr/lib/jvm/java-6-sun-1.6.0.22 (or whatever version).

Nepomuk message and contact classes

These tables are incomplete, and represent the classes and properties from the crawl of my nearly empty inbox. The full set of classes and properties for the Nepomuk ontologies are available on another page on this blog.

The overall architecture for the Semantic backed J2EE app is different from the Linked data app already discussed because we need a business logic layer and a decoupling from the persistence layer. We also want to create a Java app rather than a semantic application so that the programming paradigms and patterns are familiar to the Enterprise java developer.

Semantically backed J2EE webApp - System diagram

Here we see a fairly standard 3 tier MVC application. Browser requests URIs from the appserver, or makes an Ajax call and gets html from server side JSPs or JSON formatted data in response, respectively. The application server contains java code that maps URIs and API calls to controllers, which make calls to service classes and DAO code. The DAO code makes call via a persistence proxy to get data from the server that is unmarshalled from RDF to java objects (or makes writes in the other direction). The persistence layer is configured to use an implementation that takes care of the Object to RDF mapping – two implementations are available (JenaBean and EmpireJPA). These in turn use their own protocols to talk to native or location repositories, or typically JDBC talk with standard DBMS. Spring and Spring security provide infrastructure level services for dependency injection, component wiring, MVC abstractions, and role, method and data level security for beans and dynamically created object instances. These technologies are shown below in the AppServer layer cake.

Technology libraries and tools used in Semantically backed J2EE WebApp

Obviously, there are many things going on here, and they’ll need some discussion

MediaWiki [1] is a popular wiki system written in PHP. SemanticMediaWiki [2] is an extension to this that allows collaborate database backed site with RDF and Sparql capability. Semantic Bundle [3] extends this by adding some more extensions including semantic forms, drilldown, parallel queries, extra types and fields, result formats, and an interesting one “Halo” [4] (from Project Halo[5]) – this brings some fairly sophisticated query, form and visualisation capability.

The reason I’m looking into this is because its quite similar to Drupal7 – php, RDFa, sparql are all part of the deal and both promise to get you up and running pretty quickly with a semantic website, data mashups and syndication. But the crucial difference is that SemanticMediaWiki by nature is a collaborative thing where the structure and admin are shared among users, whereas Drupal7 has a central admin model where governance and a-priori typing are the order of the day (see [6] for early Drupal consideration in comparison to the likes of SemanticMediaWiki).

OpenID, despite its faults, allows single sign on – you can use your Google ID (and others) to sign into sites without having to create yet another username and password and divulge personal details yet again. Its useful even if it needs to change, so I thought I’d see how easy it is to set up for PHP. Sucker for punishment.

So here is the outline

install mysql

install phpMyAdmin if you like

setup a database for mediawiki

create a user with all privileges for the wiki, grant privileges, initialise database

install mediawiki

initialise the wiki database

install SemanticMediaWiki

install php5_gmp, php_curl

install openID – install php_openid, use pear.

sudo pear install MDB2mysql

sudo pear install OpenID

add Semantic Bundle extensions.

check out wibit*/exhibit [7] to visualise and create forms for RDF data

An alternative might be to install Halo, but I found that trying to use the latest version and the latest SemanticBundle was troublesome, and install documentation and packages seem not to match with documentation. Probably best to install on its own, and by the looks of it, its a good package.

*Now part of the SemanticResultFormats so that you can use the Exhibit result printer. You can use it with JSON/JSONP feeds from you local wiki, or from other SemanticMediaWiki instances if you get the other instance to create the necessary URL to plug into it. (You’ll need a CORS filter or some such on the remote side if you want to use remote JSON).

Well – what before how – this is firstly about requirements, and then about treatment

Linked Open Data app

Create a semantic repository for a read only dataset with a sparql endpoint for the linked open data web. Create a web application with Ajax and html (no server side code) that makes use of this data and demonstrates linkage to other datasets. Integrate free text search and query capability. Generate a data driven UI from ontology if possible.

So – a fairly tall order : in summary

define ontology

extract entites from digital text and transform to rdf defined by ontology

create an RDF dataset and host in a repository.

provide a sparql endpoint

create a URI namespace and resolution capability. ensure persistence and decoupling of possible

provide content negotiation for human and machine addressing

create a UI with client side code only

create a text index for keyword search and possibly faceted search, and integrate into the UI alongside query driven interfaces

the webserver (tomcat in fact) returns html and javascript. This is the “application”.

interactions on the webpage invoke javascript that either makes direct calls to Joseki (6) or makes use or permanent URIs (at purl.org) for subject instances from the ontology

purl.org redirects to dynamic dns which resolves to hosted application – on EC2, or during development to some other server. This means we have permanent URIs with flexible hosting locations, at the expense of some network round trips – YMMV.

dyndns calls EC2 where a 303 filter intersects to resolve to either a sparql (6) call for html, json or rdf. pluggable logic for different URIs and/or accept headers means this can be a select, describe, or construct.

TDB provides single semantic repository instance (java, persistent, memory mapped) addressable by joseki. For failover or horizontal scaling with multiple sparql endpoints SDB should probably be used. For vertical scaling at TDB – get a bigger machine ! Consider other repository options where physical partitioning, failover/resilience or concurrent webapp instance access required (ie if youre building a webapp connected to a repository by code rather than a web page that makes use of a sparql endpoint).

Next article will provide similar description or architecture used for the Java web application with code that is directly connected to a repository rather than one that talks to a sparql endpoint.

What needs writing ?

Now that we have an idea about what tools and technologies are available and the kind of application we want to build we need to start considering architecture and what code we will write around those tools and technologies. The architecture I planned was broadly formed – but not completely – as I went about creating these applications. I was also going to tackle the Linked Open Data webapp first and then do the Semantic Backed J2EE app. I thought MVC first for both, but went in the end with a 2 tier approach for the former, and an n-tier component based approach for the latter. (More about this in the next section). I’m used to the Spring framework, so I thought I’d go with it, and for UI I’d use jQuery and HTML and/or JSP, perhaps Velocity. But nothing was set in stone, and I was going to try and explore and be flexible.

The tools and technologies cover

creating an ontology

entity extraction

RDF generation

using RDF with Java

Semantic repositories

querying sparql end points

inference

linking data

UI and render

Category

Linked Open Data webapp

Semantic Backed J2EE webapp

creating an ontology

The ontology was going to be largely new as there is not much about to deal with historical content. Some bibliograpic ontologies are out there, but this isn’t about cataloguing books or chapters, but about the content within and across the sections in a single book. There are editions for Scotland, Wales and UK also, so I might get around to doing them at some stage. Some of the content is archaic – measurements are in Old English miles for instance. Geographic features needed to be described, along with population and natural resourcces. I wasn’t sure if I needed the expressiveness of OWL over RDFS, but thought that if I was going to start something fresh I might as well leave myself open to evolution and expansion – so OWL was the choice. Some editors dont to OWL, and in the end I settled for Protege.

Same thoughts here as for the Linked Data app – why limit myself to RDFS ? I can still do RDFS within an OWL ontology. Protege it is

entity extraction

Having played with GATE, OpenNLP, MinorThird and a foray into UIMA I settled on writing my own code. I needed close connections between my ontology, extracting the entities and generating RDF from those entities – most of these tools dont have this capability out of the box (perhaps they do now, 1 year on) – and I also wanted to minimise the number of independent steps at this point so that I could avoid writing conversion code, configure multiple parts in different ways and for different environments or OS. There is also a high barrier to entry and a long learing curve for some of these tools. I had read a lot, enough even, and wanted to get my hands dirty. I decided to build my own, based on grep – as most of these tools use regex at the bottom end and build upon it . It wasn’t going to be sophisticated, but it would be agile, best effort, experience based coding I’d be doing, and learning all the way – not a bad approach I think. I’d borrow techniques from the other tools around tokenisation and gazeteering, and if I was lucky, I might be able to use some of the ML libraries (I didnt in the end). So, with the help of Jena, I wrote components for

Processing files in directories using “tasks”, outputting to a single file, multiple files, multiple directories, different naming conventions, encoding, different RDF serialisations

Splitting single large file into sections based on a heading style used by the author. This was complicated by page indexing and numbering that a very similar style, and variations within sections that meant that end-of-section was hard to find. I got most entries out, but from time to time I find and embedded section wthin another. This can be treated individually, manually, and reimported into the repository to replace the original and create 2 in its place

Sentence tokenisation – I could have used some code from the available libraries and frameworks here, but its not too difficult, and when I did compare to the others eventually, I discovered that they also came a cropper in the same areas I did. Some manual corrections are still needed no matter how you do it, so I stuck with my own

Running regex patterns, accumulating hits in a cache. A “concept” or entity has a configuration element, and a relationship to other elements (a chain can be created).

The configuration marries an “Entity” with a “Tag”(URI). Entities are based on a delimiter, gazeteer.

Entities can be combined if they have a grouping characteristic.

An Entity can be “required” meaning that unless some “other” token is found in a sentence, the entity wont be matched. This can also be extended to having multiple required or ancialliary matches, so that a proportion need to be found (a likelihood measure) before an entity is extracted.

Some Entities can be non-matching – just echo whatever is in the input – good for debug, and for itemising raw content – I use this for echoing the sentences in the section that Im looking at – the output appears alongside the extracted entities.

The Required characteristic can also be used with Gazeteer based greps.

Entities have names that are used to match to Tags

Creating a Jena Model and adding those entities based on a configured mapping to an ontology element (URI, namespace, nested relationship, quantification (single or list, list type)

Outputting a file or appending to a file, with a configured serialisations scheme (xml/ttl/n3/…)

This was a different kind of application – here no data exists at the start, and all is created and borne digital. No extraction needed.

RDF generation

I naively started the RDF generation code as a series of string manipulations and concatenations. I thought I could get away with it, and that it would be speedy ! The RDF generation code in Jena didnt seem particularly sophisticated – the parameters are string based in the end, and you have to declare namespaces as a string etc so what could possible go wrong ?? Well, things got unwieldy, and when I wanted to validate, integrate and reuse this string manipulation code it became tedious and fractious. Configuration was prone to error. Jena at higher stages of processing then needs proper URIs and other libraries operate on that basis. So, just in time, I switched – luckily I had built the code thinking that I might end up having to alter my URI definition and RDF generation strategy, so it ended up being a discrete replacement – a new interface implementation that I could plug in.
Tags can be

lookup – based on entity value, return a particular URI – like a reverse gazeteer

Here, RDF generation isnt driven by extraction or preexisting entites, but by the Object model I used. See the next row for details.

Using RDF with Java

Fairly early on I settled with Jena as opposed to Sesame. There are some notes I found comparing Jena to Sesame1, but some of the arguments didnt mean anything to me at the early stages. There wasnt much between them I thought, but the Jena mailing list seemed a bit more active, and I noted Andy Seaborne’s name on the Sparql working group2. Both are fully featured with Sparql endpoints, repositories, text search and so on, but take different approaches3 . Since then I’ve learned a lot of course, and Ive compiled my own comparison matrix[110]. . So – I went for Jena, and I probably will in other cases, but Sesame may suit things better in others.

While Jena is Object oriented, working with it is based on RDF rather than objects. So if you have a class with properties – a bean – you have to create a Model, the Subject and add the properties and their values, along with the URIs and namespaces that they should be serialised with. You cannot hand Jena a Bean and say “give me the RDF for that object”.

For this project that wasn’t an issue – I wasnt modelling a class hierarchy, I wanted RDF from text, and then to be able to query it, and perhaps use inference. Being able to talk to Sparql endpoints and manipulate RDF was more important than modelling an Object hierarchy.

Here I needed to be able maintain parallel worlds – and Object base with a completely equivalent RDF representation. And I wanted to be able to program this from an enterprise Java developer’s perspective, rather than a logician or information analyst. How do I most easily get from Object to RDF without having to code for each triple combination [109]? Well it turns out there are 2 choices, and I ended up using one and then the other. It was also conceivable that I might not be able to do what I wanted, or that it wouldnt perform – I saw the impact of inference on query performance in the Linked Data application – so I wanted to code the app so that it would be decoupled from the persistence mechanism. I also needed to exert authorization control – could I do this with RDF ?

Java-RDF – I stuck with Jena – why give up a good thing ?

Object-RDF – Jena has 2 possibilties – JeanBean, and Jastor. I settled for JenaBean as it seemed to have support and wasnt about static class generation. This allows you to annotate your javabeans with URI and property assertions so that a layer of code can create the RDF for you dynamically, and then do the reverse when you want to query.

AdHoc Sparql – the libraries work OK when you are asking for Objects by ID, but if you want Objects that have certain property values orconditions then you need to write Sparql and submit that to the library.

So, I could build my app in an MVC style, and treat the domain objects much like I would if I used Hibernate or JDO say. In addition, I could put in a proxy layer so that the services werent concerned about which persistence approach I took – if I wanted, I could revert to traditional RDBMS persistence if I wanted. So I could haveView code, controllers, domain objects (DAO), service classes, a persistence layer consisting of a proxy and an Object to RDF implemenation.

I built this, and soon saw that RDF repositories, in particular Jena SDB, when used with JenaBean are slow. This boils down to the fact that SPARQL ultimatey is translated to SQL, and some SPARQL operations have to be performed client side. When you do this in an Object to RDF fashion, where every RDF statement ends up as a SQL join or independent query, you get a very very chatty storage layer. This isn’t uncommon in ORM land and lazy loading is used so that for instance, a property isnt retrieved until its actually needed – eg if a UI action needs to show a particular object property in addition to showing that an object exists. In the SPARQL case, there are more things that need to be done client side, like filtering, and this means that a query may retrieve (lots) more information than its actually going to need to create a query solution, and the processing of the solution is going to take place in your application JVM and not in the repository.

I wanted then to see if the performance was significantly better with a local repository even if it couldnt be addressed from multiple application instances (TDB), and if Sesame was any better. TDB turned out to be lots faster, but of course you cant have multiple webapps talking to it unless you use address it as a Sparql endpoint- not an Object in Java code. For Sesame tho, I needed to ditch JenaBean, and luckily, in the time I had been building the application a new Java Object-RDF middleware came out, called Empire-JPA[72].

This allows you to program your application in much the same way as JeanBean – annotations and configuration – but uses the JPA api to persist objects to a variety of backends. So I could mark up my beans with Empire Annotations (leaving the JenaBean ones in place) and in theory persist the RDF to TDB, SDB, any of the Sesame backends, FourStore and so on.

The implementation was slowed down because the SDB support wasn’t there, and the TDB support needed some work, but it was easy to work Mike Grove at ClarkParsia on this, and it was a breath of fresh air to get some good helpful support, an open attitude, and timely responses.

I discovered along the way that I couldn’t start with a JenaBean setup, persist my objects to TDB say, and switch seamlessly to Empire-JPA (or vice versa). It seems that JenaBean persists some configuration statements and these interfere with Empire in some fashion – but this is an unlikely thing to do in production, so I havent followed it thru.

Empire is also somewhat slower than JenaBean when it comes to complex object hierarchies, but Mike is working on this, and v 0.7 includes the first tranche of improvements.

Doing things with JPA has the added benefit of giving you the opportunity to revert to RDBMS or to start with RDBMS and try out RDF in parts, or do both. It also means that you have lots of documentation and patterns to follow, and you can work with a J2EE standard which you are familiar with.

But, in the end Semantic Repositories aren’t as quick as SQL-RDBMS, but if you want RDF storage for some of your data or for a subset of your functionality, a graph based dataset, a common schema, vocabulary (or parts of) for you and other departments or companies in your business circle, and the distinct advantage of inference for data mining, relationship expressiveness (“similar” or other soft equivalences rather than just “same”) and discovery.

A note about authorization (ACL) and security: None of the repositories I’ve come across have access control capabilities along the lines of what you might see with an RDBMS – grant authorities and restrictions just aren’t there. (OpenVirtuoso may have something as it has a basis in RDBMS (?)).

You might be able to do some query restriction based on graphs by making use of a username, but if you want to say make sure that a field containing a social securrty number is only visible to the owner or application administrator (or some other Role) but not to other users, then you need to do that ACL at the application level. I did this in Spring with Spring Security (Acegi), at the object level. Annotations and AOP can be used to set this up for Roles, controllers, Spring beans (that is beans under control of a Spring context) or beans dynamically created (eg Domain objects created by controllers) . ACL and authentication in Spring depend on a User definition, so I also had to create an implementation that retrieved User objects from the semantic repository, but once that was done, it was an ACL manipulation problem rather than an RDF one.

The result was a success, if you can ignore the large dataset performance concerns. A semantic respository can easily and successfully be used for persistence storage in a Java J2EE application built around DAO, JPA and Service patterns, with enterprise security and access control, while also providing a semantic query capability for advanced and novel information mining, discovery and exploration.

Semantic repositories

This application ultimately needs to be able to support lots of concurrent queries – eg +20 per sec, per instance. Jena uses Multiple Reader Single Writer approach for this, so should be fine. But with inference things slow down a lot, and memory needs to be available to service concurrent queries and datasets. The Amazon instance I have for now uses a modest 600mB for Heap, but with inference could use lots more, and a lot of CPU. Early on I used a 4 year old Dell desktop to run TDB and Joseki, and queries would get lost in it and never return – or so I thought. Moving to a Pentium Duo made things better, but its easy to write queries that tie up the whole dataset when youre not a sparql expert and can in some cases can cause the JVM to OoM and/or bomb. SDB suffers (as mentioned in the previous section) and any general purpose RDBMS hosted semantic repository that has to convert from SPARQL to SQL and back-and-forth will have performance problems. But for this application, TDB currently suffices – I dont have multiple instances of a Java application and if did host the html/js on another instance (a tomcat cluster say) then it would work perfectly well with Joseki in front of TDB or SDB. On the downside, an alternative to Jena is not a real possibility here as the Sparql in the pagecode makes heavy use of Jena ARQ extensions for counts and other aggregate functions. Sparql 1.1 specifies these things, so perhaps in future it will be a possibility.

As a real java web application one of the primary requirements here is that the repository is addressable using java code from multiple instances1. TDB doesnt allow this because you define it per JVM. Concurrent access leads to unpredictable results, to put it politely. SDB would do it, as the database takes care of the ACIDity, but its slow.

I also wanted to be able to demonstrate the application and test performance with RDBMS technology or Semantic Repository, or indeed NoSQL technology. The class hierarchy and componentisation allows this, but at this stage I’ve not tried going back to RDBMS or the NoSQL route. Empire-JPA allows a variety of repositories to be used, and those based on Sesame include OWLIM and BigData which seem to offer large scale and clustered repository capability. To use AllegroGraph or Rdf2Go would require another implementation of my Persitence Layer, and may require more bean annotations.

So, nothing is perfect, everything is “slow”, but flexibility is available.

1. It might be possible to treat the repository as remote datasource and use SPARQL Select and Insert/Update queries (and this may be more performant it turns out), but for this excerise I wanted to stick with tradition and build a J2EE application that didnt have hard coded queries (or externalised and mapped ones a la iBatis) but that encapsulated the business logic and entity as bean and service object base.

querying sparql end points

inference

linking data

More important here than in the J2EE webapp, being able to host a dataset on the Linked Data Web with 303 Redirect, permanent urls, slash rather than hash URIs and content negotiation meant that I ended up with Joseki as the Sparql endpoint, and a servlet filter within a base webapp that did the URI rewriting, 303 redirect and content negotiation. Ontology and instance URIs can be serviced by loading the Ontology into the TDB repository. The application is read only, so theres no need for the Joseki insert/update servlet. I also host an ancillariy dataset for townlands so that I can keep it distinct for use with other applications, but federate in with an ARQ Service keyword. Making links between extracted entities and geoNames, dbPedia and any other dataset is done as a decorator object in the extraction pipeline. Jena’s SPARQL objects are used for this, but in the case of the Geonames webservice, their Java client library is used.

One of the issues here of course is cross-domain scripting. Making client side requests to code from another domain (or making Ajax calls to another domain) isnt allowed by modern UserAgents unless they support JSONP or CORS. Both require an extra effort on the part of the remote data provider and could do with some seamless support (or acknowledgement at least) from the UI javascript libraries. It happens that Jetty7 has a CORS filter (which I retrofitted to Joseki 3.4.2 [112]). JSONP can be fudged with jQuery it turns out, if the remote dataset provides JSON output – some don’t. The alternative is that for anyone wishing to use your dataset on the Linked Open Data web, that they must implement a server side proxy of some kind and (usually) work with RSF/XML. A lot of web developers and mashup artists will baulk at this, but astonishngly, post Web2.,0, they still seem to be out of the reach of many dataset publishers. Jetty7 with its CORS fitler goes a long way to improving this situation, but it would be great to see it in Tomcat too, so that publishers don’t have to implement what is a non-trivial filter (this is a security issue after all), and clients dont have to revert (or find/hire/blackmail) to server side code and another network hop.

Vladimir Dzhuvinov has another CORS filter [111], that adds request-tagging and Access-Control-Expose-Headers in the response.

The only need of Sparql endpoint here is for debug purposes. You need to be able to see the triples as the repository sees them when you use an ORdfM layer so that you can understand the queries that are generated, why some of your properties arent showing up and so on.

For query handling I needed a full featured console that would allow me inference (performance permitting) and allow me to render results efficiently. I also needed to be able to federate queries across datasets or endpoints – especially to UMBEL so that I could offer end users the ability to locate data tagged with an UMBEL URI that were “similar” to one they were intersted in (eg sharing a skos:broader statement) . Jena provides the best support here in terms of SPARQL extensions, but inference was too slow for me, and I could mimic some of the basic inference with targetted query writing for Sesame. Sesame doesnt do well with aggregate functions, and inference is per repository and on-write, so you need to adjust how you view the repository compared to how Jena does it. Sesame is faster with an in-memory database.

UI and render

This is an exercise in HTML and Ajax. It’s easy to issue Sparql queries that are generated in Javascript based on the what needs to be done, but theres one for every action on the website, and its embedded in the code. Thats not a huge problem given the open nature of the dataset and the limited functionality thats being offered (the extraction process only deals with a small subset of the available information in the text). jQuery works well with Joseki, local or not [112] so the JSON/JSONP issue didnt arise for me. Getting a UI based on the Ontology was possible using the jOWL javascript library, but its not the prettiest or most intuituve to use. A more sophisticated UI would need lots more work, and someone with an eye for web page design 🙂

Here, the UI is generated with JSP code with embedded JS/Ajax calls back to the API. URLs are mapped to JSP and Role based access control enforced. Most URLs have to be authenticated. Spring has a Jackson JSON view layer so that the UI could just work with Javascript arrays, but this requires more annotations on the beans for some properties that cause circular references. The UI code is fairly unsophisticated and for the sake of genericity, it mostly just spits out what is in the array, assuming that the annotations have taken care of most of the filtering, and that the authorization code has done its work and cloaked location, identity and datetime information. The latter works perfectly well, but some beans have propoerties that a real user wouldnt be interested in.

Velocity is used in some places when a user sends a message or invitation, but this is done at the object layer.

The UI doesnt talk Sparql to any endpoint. Sparql queries are generated based on end user actions (the query and reporting console), but this is done at the Java level.

I’ve filled out the tools matrix with the 60 or so tools, libraries and frameworks I looked at for the two projects I created. Not all are used of course, and only a few are used in both. Includes comments and opinion, which I used and why, and all referenced. Phew.